1 Introduction

Moving to a new city on a tight budget is challenging. Especially, a metropolis like London has high rents and a competitive market that makes it difficult to find accommodation at a reasonable price with just the attributes that you are looking for. Sharing economy services like Airbnb have faciliated the search for a nice room rented out by private agent. The rooms and apartments available are furnished for the user to settle right in. But how do you know if the price you are paying for your flat is acutally a fair price?

Profit of both hosts and the platform itself have skyrocketed in the past years. A typical UK host earns around 3,000 Pound a year (Cox, 2017a). It is certain that profit comes from the user that is paying both the fee of the platform and the profit margin of the host out of his own pocket. If you are on a tight budget yourself you want to pick a price that is market average with the attributes important to you. This paper aims at creating a model to forecast the price a user pay will pay per person per night for an Airbnb matching his requirements to faciliate the check whether the price of the apartment is indeed the fair price.

2 Description of the dataset

The dataset for this investigation covers all Airbnb offerings in London as per the 4th and 5th of March 2017. It contains 53.904 objects for 95 different variables. Its source is the website “Inside Airbnb - Adding data to the debate” (Cox, 2017b). This in an independent and non-commercial project aimed to examine the effect of Airbnb activities on urban development.

Prior to using the dataset for this project, some adjustments and additions had to be made. The operations conducted and the resulting data can be grouped as follows:

  • Price: Reflects the price per night as offered on Airbnb.
  • Rent per Zipcode: The initial dataset holds no information on the regular rent price at the location of an Airbnb. Rent prices however, can be considered as the major cost of providing an Airbnb. Therefore, we used the first half of the Zipcode to apply the average rent for a single bedroom flat to every issuing. The rent information was drawn from SOURCE.
  • Distance / Location: Another important aspect of an Airbnb is its location in terms of distance to the city centre. Considering Picadilly Circus (Longitude: -0.133869, Latitude: 51.510067) as the touristic centre of London, we calculated the linear distance for every Airbnb using the Haversine formula. Furthermore, the Cartesian coordinates were calculated, using the instructions of Irawan (2014), to plot the data on maps provided by Lovelace & Cheshire (2014).
  • Reviews: Reflect the average customer reviews from Airbnb and the total number of reviews.
  • Property Characteristics: Some general information on the property such as the room type, the number of people that can be accommodated or the number of bathrooms.
  • Amenities: On top of the characteristics, Airbnb contains information on a wide range of amenities for every flat. These range from the availability of Internet and a TV up to a personal doorman or a pool. We introduced dummy variables for 52 different amenities as well as a variable counting the total number of amenities.
  • Offering Characteristics: Lastly some information on the Cancellation Policy or whether the Airbnb is instantly bookable was included.

To allowe this investigation to be more focused, on its actual goal of helping students to find the right place for their desired Airbnb, some filters were applied as well. Therefore, only apartments with a private room and at least three valid ratings were included. The resulting dataset has 78 variables and 6.495 objects.

3 Mapping

Bibliography

Cox, J. (2017a) Airbnb: Surge in uk hosts over past year boosts local economies. The Independent. [Online] Available from: http://www.independent.co.uk/news/business/news/airbnb-hosts-uk-surge-boost-local-economies-online-holiday-rental-london-southwest-northern-ireland-a7940451.html.

Cox, M. (2017b) Inside airbnb - adding data to the debate. [Online]. Available from: http://data.insideairbnb.com/united-kingdom/england/london/2017-03-04/data/listings.csv.gz.

Irawan, D.E. (2014) How to convert lat-long coordinates to utm. [Online]. Available from: https://rpubs.com/dasaptaerwin/19879.

Lovelace, R. & Cheshire, J. (2014) Introduction to visualising spatial data in R. National Centre for Research Methods Working Papers. [Online] 14 (03). Available from: https://github.com/Robinlovelace/Creating-maps-in-R.

Appendix

Column Numbers Name Description
1 price Price per Nighty as offered on Airbnb
2 zip_first First half of the London Zipcode
3 mean_rent Mean Rent for the given Zipcode as per SOURCE
4 distance Distance from Picadilly Circus in km
5 - 6 east & north Geographic Cartesian coordinates required for map plotting
7 - 13 review_scores Average customer reviews from Airbnb
14 number_of_reviews Number of customer reviews
15 property_type …
16 room_type …
17 accommodates …
18 bathrooms …
19 bedrooms …
20 beds …
21 amenities_count …
22 - 74 amen Dummy Variables for the various amenities
75 minimum_nights …
76 instant_bookable …
77 cancellation_policy …

Imperial College Business School